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首页> 外文期刊>International Journal of Innovative Computing Information and Control >3D ONLINE PATH PLANNING OF UAV BASED ON IMPROVED DIFFERENTIAL EVOLUTION AND MODEL PREDICTIVE CONTROL
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3D ONLINE PATH PLANNING OF UAV BASED ON IMPROVED DIFFERENTIAL EVOLUTION AND MODEL PREDICTIVE CONTROL

机译：基于改进的微分进化和模型预测控制的无人机3D在线路径规划

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摘要

This paper presents an efficient 3D online path planning algorithm for UAV flying in partially known environment. The algorithm integrates model predictive control (MPC) and differential evolution (DE) as the planning strategy. In the initial stage, the artificial potential field (APF) model is developed to describe the mutual effect between the UAV and the surrounding environments. Afterwards, a novel objective function is proposed to address the optimization problem of multi-objective and multi-constraints, which take into account the path length, the smoothness degree of a path and the safety of a path. In addition, the multiple constraints based on the realistic scenarios are taken into account, including maximum acceleration, maximum velocity, map and threat constraints. Then, the improved differential evolution algorithm based on the theory of MPC, is developed to optimize the objective function to find the optimal path. Finally, to show the high performance of the proposed method, we compare the proposed algorithm with the existing optimization algorithms and several extended algorithms. The results reveal that the proposed algorithm not only produces an optimal plan for UAV in a local known 3D environment, but also has better performances in terms of running time and stability.
机译：本文提出了一种用于无人机在部分已知环境中飞行的有效3D在线路径规划算法。该算法将模型预测控制（MPC）和差异演化（DE）集成为计划策略。在初始阶段，开发了人工势场（APF）模型来描述无人机与周围环境之间的相互作用。然后，提出了一种新颖的目标函数来解决多目标和多约束的优化问题，它考虑了路径长度，路径的平滑度和路径的安全性。此外，还考虑了基于实际场景的多重约束，包括最大加速度，最大速度，地图和威胁约束。然后，基于MPC理论，提出了一种改进的差分进化算法，对目标函数进行优化，找到最优路径。最后，为了展示该方法的高性能，我们将其与现有的优化算法和几种扩展算法进行了比较。结果表明，所提出的算法不仅在局部已知的3D环境中为无人机提供了最佳方案，而且在运行时间和稳定性方面都具有更好的性能。

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